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Soni N, Ora M, Jena A, Rana P, Mangla R, Ellika S, Almast J, Puri S, Meyers SP. Amino Acid Tracer PET MRI in Glioma Management: What a Neuroradiologist Needs to Know. AJNR Am J Neuroradiol 2023; 44:236-246. [PMID: 36657945 PMCID: PMC10187808 DOI: 10.3174/ajnr.a7762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/21/2022] [Indexed: 01/21/2023]
Abstract
PET with amino acid tracers provides additional insight beyond MR imaging into the biology of gliomas that can be used for initial diagnosis, delineation of tumor margins, planning of surgical and radiation therapy, assessment of residual tumor, and evaluation of posttreatment response. Hybrid PET MR imaging allows the simultaneous acquisition of various PET and MR imaging parameters in a single investigation with reduced scanning time and improved anatomic localization. This review aimed to provide neuroradiologists with a concise overview of the various amino acid tracers and a practical understanding of the clinical applications of amino acid PET MR imaging in glioma management. Future perspectives in newer advances, novel radiotracers, radiomics, and cost-effectiveness are also outlined.
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Affiliation(s)
- N Soni
- From the University of Rochester Medical Center (N.S., S.E., J.A., S.P., S.M.), Rochester, New York
| | - M Ora
- Sanjay Gandhi Postgraduate Institute of Medical Sciences (M.O.), Lucknow, Uttar Pradesh, India
| | - A Jena
- Indraprastha Apollo Hospital (A.J., P.R.), New Delhi, India
| | - P Rana
- Indraprastha Apollo Hospital (A.J., P.R.), New Delhi, India
| | - R Mangla
- Upstate University Hospital (R.M.), Syracuse, New York
| | - S Ellika
- From the University of Rochester Medical Center (N.S., S.E., J.A., S.P., S.M.), Rochester, New York
| | - J Almast
- From the University of Rochester Medical Center (N.S., S.E., J.A., S.P., S.M.), Rochester, New York
| | - S Puri
- From the University of Rochester Medical Center (N.S., S.E., J.A., S.P., S.M.), Rochester, New York
| | - S P Meyers
- From the University of Rochester Medical Center (N.S., S.E., J.A., S.P., S.M.), Rochester, New York
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2
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von Rohr K, Unterrainer M, Holzgreve A, Kirchner MA, Li Z, Unterrainer LM, Suchorska B, Brendel M, Tonn JC, Bartenstein P, Ziegler S, Albert NL, Kaiser L. Can Radiomics Provide Additional Information in [18F]FET-Negative Gliomas? Cancers (Basel) 2022; 14:cancers14194860. [PMID: 36230783 PMCID: PMC9612387 DOI: 10.3390/cancers14194860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Amino acid positron emission tomography (PET) complements standard magnetic resonance imaging (MRI) since it directly visualizes the increased amino acid transport into tumor cells. Amino acid PET using O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) has proven to be relevant, for example, for glioma classification, identification of tumor progression or recurrence, or for the delineation of tumor extent. Nevertheless, a relevant proportion of low-grade gliomas (30%) and few high-grade gliomas (5%) were found to show no or even decreased amino acid uptake by conventional visual analysis of PET images. Advanced image analysis with the extraction of radiomic features is known to provide more detailed information on tumor characteristics than conventional analyses. Hence, this study aimed to investigate whether radiomic features derived from dynamic [18F]FET PET data differ between [18F]FET-negative glioma and healthy background and thus provide information that cannot be extracted by visual read. Abstract The purpose of this study was to evaluate the possibility of extracting relevant information from radiomic features even in apparently [18F]FET-negative gliomas. A total of 46 patients with a newly diagnosed, histologically verified glioma that was visually classified as [18F]FET-negative were included. Tumor volumes were defined using routine T2/FLAIR MRI data and applied to extract information from dynamic [18F]FET PET data, i.e., early and late tumor-to-background (TBR5–15, TBR20–40) and time-to-peak (TTP) images. Radiomic features of healthy background were calculated from the tumor volume of interest mirrored in the contralateral hemisphere. The ability to distinguish tumors from healthy tissue was assessed using the Wilcoxon test and logistic regression. A total of 5, 15, and 69% of features derived from TBR20–40, TBR5–15, and TTP images, respectively, were significantly different. A high number of significantly different TTP features was even found in isometabolic gliomas (after exclusion of photopenic gliomas) with visually normal [18F]FET uptake in static images. However, the differences did not reach satisfactory predictability for machine-learning-based identification of tumor tissue. In conclusion, radiomic features derived from dynamic [18F]FET PET data may extract additional information even in [18F]FET-negative gliomas, which should be investigated in larger cohorts and correlated with histological and outcome features in future studies.
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Affiliation(s)
- Katharina von Rohr
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Marcus Unterrainer
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | | | - Zhicong Li
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Lena M. Unterrainer
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | | | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
| | - Joerg-Christian Tonn
- Department of Neurosurgery, University Hospital, LMU Munich, 81377 Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Sibylle Ziegler
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Nathalie L. Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
- Correspondence: (N.L.A.); (L.K.)
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
- Correspondence: (N.L.A.); (L.K.)
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3
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Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach. Cancers (Basel) 2022; 14:cancers14061475. [PMID: 35326626 PMCID: PMC8945893 DOI: 10.3390/cancers14061475] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/11/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Glioblastomas carry a poor prognosis and usually presents with heterogeneous regions in the brain tumor. Multi-parametric MR images can show morphological characteristics. Radiomics features refer to the extraction of a large number of quantitative measurements that describe the geometry, intensity, and texture which were extracted from contrast-enhanced T1-weighted images from anatomical MRI and metabolic features from PET. It also provides a qualitative image interpretation as well as cellular, molecular, and tumor properties. Thus, it derives additional information about the entire tumor volume which is generally of irregular shape and size from routinely evaluated “non-invasive” imaging biomarkers techniques. We demonstrated volumetric habitats and signatures in necrosis, solid tumor, peritumoral tissue, and edema with key biological processes and phenotype features. This provides physicians with key information on how the disease is progressing in the brain and can also give an indication of how well treatment is working. Abstract Glioblastoma (GBM) is a fast-growing and aggressive brain tumor of the central nervous system. It encroaches on brain tissue with heterogeneous regions of a necrotic core, solid part, peritumoral tissue, and edema. This study provided qualitative image interpretation in GBM subregions and radiomics features in quantitative usage of image analysis, as well as ratios of these tumor components. The aim of this study was to assess the potential of multi-parametric MR fingerprinting with volumetric tumor phenotype and radiomic features to underlie biological process and prognostic status of patients with cerebral gliomas. Based on efficiently classified and retrieved cerebral multi-parametric MRI, all data were analyzed to derive volume-based data of the entire tumor from local cohorts and The Cancer Imaging Archive (TCIA) cohorts with GBM. Edema was mainly enriched for homeostasis whereas necrosis was associated with texture features. The proportional volume size of the edema was about 1.5 times larger than the size of the solid part tumor. The volume size of the solid part was approximately 0.7 times in the necrosis area. Therefore, the multi-parametric MRI-based radiomics model reveals efficiently classified tumor subregions of GBM and suggests that prognostic radiomic features from routine MRI examination may also be significantly associated with key biological processes as a practical imaging biomarker.
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Krauze AV, Camphausen K. Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition. Int J Mol Sci 2021; 22:13278. [PMID: 34948075 PMCID: PMC8703419 DOI: 10.3390/ijms222413278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/02/2021] [Accepted: 12/03/2021] [Indexed: 11/30/2022] Open
Abstract
Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.
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Affiliation(s)
- Andra V. Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, Bethesda, MD 20892, USA;
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Shi L, Liu X, Wu K, Sun K, Lin C, Li Z, Zhao S, Fan X. Surface values, volumetric measurements and radiomics of structural MRI for the diagnosis and subtyping of attention-deficit/hyperactivity disorder. Eur J Neurosci 2021; 54:7654-7667. [PMID: 34614247 DOI: 10.1111/ejn.15485] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/22/2021] [Accepted: 10/03/2021] [Indexed: 11/28/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is diagnosed subjectively based on an individual's behaviour and performance. The clinical community has no objective biomarker to inform the diagnosis and subtyping of ADHD. This study aimed to explore the potential diagnostic biomarkers of ADHD among surface values, volumetric metrics and radiomic features that were extracted from structural MRI images. Public data of New York University and Peking University were downloaded from the ADHD-200 Consortium. MRI T1-weighted images were pre-processed using CAT12. We calculated surface values based on the Desikan-Killiany atlas. The volumetric metrics (mean grey matter volume and mean white matter volume) and radiomic features within each automated anatomical labelling (AAL) brain area were calculated using DPABI and IBEX, respectively. The differences among three groups of participants were tested using ANOVA or Kruskal-Wallis test depending on the normality of the data. We selected discriminative features and classified typically developing controls (TDCs) and ADHD patients as well as two ADHD subtypes using least absolute shrinkage and selection operator and support vector machine algorithms. Our results showed that the radiomics-based model outperformed the others in discriminating ADHD from TDC and classifying ADHD subtypes (area under the curve [AUC]: 0.78 and 0.94 in training test; 0.79 and 0.85 in testing set). Combining grey matter volumes, surface values and clinical factors with radiomic features can improve the performance for classifying ADHD patients and TDCs with training and testing AUCs of 0.82 and 0.83, respectively. This study demonstrates that MRI T1-weighted features, especially radiomic features, are potential diagnostic biomarkers of ADHD.
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Affiliation(s)
- Liting Shi
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China
| | - Xuechun Liu
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Keqing Wu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China.,School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Kui Sun
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Chunsen Lin
- Department of Radiology, Taian Disabled Soldiers' Hospital of Shandong Province, Tai'an, China
| | - Zhengmei Li
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Shuying Zhao
- Medical Engineering and Technology Research Center; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China.,The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xiuqin Fan
- Laboratory of Nutrition and Development, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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Li Z, Kaiser L, Holzgreve A, Ruf VC, Suchorska B, Wenter V, Quach S, Herms J, Bartenstein P, Tonn JC, Unterrainer M, Albert NL. Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [ 18F]FET PET radiomics. Eur J Nucl Med Mol Imaging 2021; 48:4415-4425. [PMID: 34490493 PMCID: PMC8566644 DOI: 10.1007/s00259-021-05526-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/05/2021] [Indexed: 12/22/2022]
Abstract
Purpose To evaluate radiomic features extracted from standard static images (20–40 min p.i.), early summation images (5–15 min p.i.), and dynamic [18F]FET PET images for the prediction of TERTp-mutation status in patients with IDH-wildtype high-grade glioma. Methods A total of 159 patients (median age 60.2 years, range 19–82 years) with newly diagnosed IDH-wildtype diffuse astrocytic glioma (WHO grade III or IV) and dynamic [18F]FET PET prior to surgical intervention were enrolled and divided into a training (n = 112) and a testing cohort (n = 47) randomly. First-order, shape, and texture radiomic features were extracted from standard static (20–40 min summation images; TBR20–40), early static (5–15 min summation images; TBR5–15), and dynamic (time-to-peak; TTP) images, respectively. Recursive feature elimination was used for feature selection by 10-fold cross-validation in the training cohort after normalization, and logistic regression models were generated using the radiomic features extracted from each image to differentiate TERTp-mutation status. The areas under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value were calculated to illustrate diagnostic power in both the training and testing cohort. Results The TTP model comprised nine selected features and achieved highest predictability of TERTp-mutation with an AUC of 0.82 (95% confidence interval 0.71–0.92) and sensitivity of 92.1% in the independent testing cohort. Weak predictive capability was obtained in the TBR5–15 model, with an AUC of 0.61 (95% CI 0.42–0.80) in the testing cohort, while no predictive power was observed in the TBR20–40 model. Conclusions Radiomics based on TTP images extracted from dynamic [18F]FET PET can predict the TERTp-mutation status of IDH-wildtype diffuse astrocytic high-grade gliomas with high accuracy preoperatively. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05526-6.
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Affiliation(s)
- Zhicong Li
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Viktoria C Ruf
- Center for Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Bogdana Suchorska
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
- Department of Neurosurgery, Sana Hospital, Duisburg, Germany
| | - Vera Wenter
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Stefanie Quach
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Jochen Herms
- Center for Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jörg-Christian Tonn
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marcus Unterrainer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Nathalie L Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Cui M, Zorrilla-Veloz RI, Hu J, Guan B, Ma X. Diagnostic Accuracy of PET for Differentiating True Glioma Progression From Post Treatment-Related Changes: A Systematic Review and Meta-Analysis. Front Neurol 2021; 12:671867. [PMID: 34093419 PMCID: PMC8173157 DOI: 10.3389/fneur.2021.671867] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 03/24/2021] [Indexed: 11/24/2022] Open
Abstract
Purpose: To evaluate the diagnostic accuracy of PET with different radiotracers and parameters in differentiating between true glioma progression (TPR) and post treatment-related change (PTRC). Methods: Studies on using PET to differentiate between TPR and PTRC were screened from the PubMed and Embase databases. By following the PRISMA checklist, the quality assessment of included studies was performed, the true positive and negative values (TP and TN), false positive and negative values (FP and FN), and general characteristics of all the included studies were extracted. Results of PET consistent with reference standard were defined as TP or TN. The pooled sensitivity (Sen), specificity (Spe), and hierarchical summary receiver operating characteristic curves (HSROC) were generated to evaluate the diagnostic accuracy. Results: The 33 included studies had 1,734 patients with 1,811 lesions suspected of glioma recurrence. Fifteen studies tested the accuracy of 18F-FET PET, 12 tested 18F-FDG PET, seven tested 11C-MET PET, and three tested 18F-DOPA PET. 18F-FET PET showed a pooled Sen and Spe of 0.88 (95% CI: 0.80, 0.93) and 0.78 (0.69, 0.85), respectively. In the subgroup analysis of FET-PET, diagnostic accuracy of high-grade gliomas (HGGs) was higher than that of mixed-grade gliomas (P interaction = 0.04). 18F-FDG PET showed a pooled Sen and Spe of 0.78 (95% CI: 0.71, 0.83) and 0.87 (0.80, 0.92), the Spe of the HGGs group was lower than that of the low-grade gliomas group (0.82 vs. 0.90, P = 0.02). 11C-MET PET had a pooled Sen and Spe of 0.92 (95% CI: 0.83, 0.96) and 0.78 (0.69, 0.86). 18F-DOPA PET had a pooled Sen and Spe of 0.85 (95% CI: 0.80, 0.89) and 0.70 (0.60, 0.79). FET-PET combined with MRI had a pooled Sen and Spe of 0.88 (95% CI: 0.78, 0.94) and 0.76 (0.57, 0.88). Multi-parameters analysis of FET-PET had pooled Sen and Spe values of 0.88 (95% CI: 0.81, 0.92) and 0.79 (0.63, 0.89). Conclusion: PET has a moderate diagnostic accuracy in differentiating between TPR and PTRC. The high Sen of amino acid PET and high Spe of FDG-PET suggest that the combination of commonly used FET-PET and FDG-PET may be more accurate and promising, especially for low-grade glioma.
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Affiliation(s)
- Meng Cui
- Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Neurosurgery, The First Medical Centre of Chinese People's Liberation Army General Hospital, Beijing, China
| | - Rocío Isabel Zorrilla-Veloz
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- The University of Texas MD Anderson Cancer Centre UT Health Graduate School of Biomedical Sciences, Houston, TX, United States
| | - Jian Hu
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- The University of Texas MD Anderson Cancer Centre UT Health Graduate School of Biomedical Sciences, Houston, TX, United States
| | - Bing Guan
- Department of Health Economics, The First Medical Centre of Chinese People's Liberation Army General Hospital, Beijing, China
| | - Xiaodong Ma
- Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Neurosurgery, The First Medical Centre of Chinese People's Liberation Army General Hospital, Beijing, China
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8
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Unterrainer M, Ruf V, von Rohr K, Suchorska B, Mittlmeier LM, Beyer L, Brendel M, Wenter V, Kunz WG, Bartenstein P, Herms J, Niyazi M, Tonn JC, Albert NL. TERT-Promoter Mutational Status in Glioblastoma - Is There an Association With Amino Acid Uptake on Dynamic 18F-FET PET? Front Oncol 2021; 11:645316. [PMID: 33996563 PMCID: PMC8121001 DOI: 10.3389/fonc.2021.645316] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/26/2021] [Indexed: 12/19/2022] Open
Abstract
Objective The mutation of the ‘telomerase reverse transcriptase gene promoter’ (TERTp) has been identified as an important factor for individual prognostication and tumorigenesis and will be implemented in upcoming glioma classifications. Uptake characteristics on dynamic 18F-FET PET have been shown to serve as additional imaging biomarker for prognosis. However, data on the correlation of TERTp-mutational status and amino acid uptake on dynamic 18F-FET PET are missing. Therefore, we aimed to analyze whether static and dynamic 18F-FET PET parameters are associated with the TERTp-mutational status in de-novo IDH-wildtype glioblastoma and whether a TERTp-mutation can be predicted by dynamic 18F-FET PET. Methods Patients with de-novo IDH-wildtype glioblastoma, WHO grade IV, available TERTp-mutational status and dynamic 18F-FET PET scan prior to any therapy were included. Here, established clinical parameters maximal and mean tumor-to-background-ratios (TBRmax/TBRmean), the biological-tumor-volume (BTV) and minimal-time-to-peak (TTPmin) on dynamic PET were analyzed and correlated with the TERTp-mutational status. Results One hundred IDH-wildtype glioblastoma patients were evaluated; 85/100 of the analyzed tumors showed a TERTp-mutation (C228T or C250T), 15/100 were classified as TERTp-wildtype. None of the static PET parameters was associated with the TERTp-mutational status (median TBRmax 3.41 vs. 3.32 (p=0.362), TBRmean 2.09 vs. 2.02 (p=0.349) and BTV 26.1 vs. 22.4 ml (p=0.377)). Also, the dynamic PET parameter TTPmin did not differ in both groups (12.5 vs. 12.5 min, p=0.411). Within the TERTp-mutant subgroups (i.e., C228T (n=23) & C250T (n=62)), the median TBRmax (3.33 vs. 3.69, p=0.095), TBRmean (2.08 vs. 2.09, p=0.352), BTV (25.4 vs. 30.0 ml, p=0.130) and TTPmin (12.5 vs. 12.5 min, p=0.190) were comparable, too. Conclusion Uptake characteristics on dynamic 18F-FET PET are not associated with the TERTp-mutational status in glioblastoma However, as both, dynamic 18F-FET PET parameters as well as the TERTp-mutation status are well-known prognostic biomarkers, future studies should investigate the complementary and independent prognostic value of both factors in order to further stratify patients into risk groups.
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Affiliation(s)
- Marcus Unterrainer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Viktoria Ruf
- Department of Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Katharina von Rohr
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Bogdana Suchorska
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | | | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Vera Wenter
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Wolfgang G Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jochen Herms
- Department of Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Jörg C Tonn
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Nathalie Lisa Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
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9
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Galldiks N, Zadeh G, Lohmann P. Artificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology. Neurooncol Adv 2020; 2:iv1-iv2. [PMID: 33521635 PMCID: PMC7829469 DOI: 10.1093/noajnl/vdaa179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Norbert Galldiks
- Institute of Neuroscience and Medicine (INM-3, INM-4), Research Center Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany
| | - Gelareh Zadeh
- Princess Margaret Cancer Center and MacFeeters-Hamilton Center for Neuro-Oncology Research, University Health Network, Wilkins Family Chair in Brain Tumor Research, Toronto, Canada
- Division of Neurosurgery, Toronto Western Hospital, Toronto, Canada
- Krembil Brain Institute, Toronto, Canada
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, INM-4), Research Center Juelich, Juelich, Germany
- Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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